The present invention generally relates to the ability to detect and recognize objects hidden behind porous occluders, such as foliage and camouflage, thereby enhancing operations in public safety, law enforcement, and defense. While any instantaneous view of the scene might contain rays hitting only a fraction of the object surface of interest, different fragments could be exposed by moving the object, the occluder, or the sensor. Theoretically, the aggregation of a diverse set of views should yield enough information to reconstruct the whole object. But achieving this goal with a 2D sensor is impractical for three reasons: the pores may be smaller than the sensor's pixel resolution, insufficient light from the object may reach the sensor, and segmenting a 2D image into object and occluder pixels is difficult.
In contrast, a 3D ladar imaging system can record multiple range echoes from a single laser pulse, enabling detection of concealed objects through pores smaller than the beam width. Ladar receivers can have high photon sensitivity yet not be overwhelmed by noise. The measured range values immediately provide foreground/background segmentation. A frame of ladar data is a collection of 3D points measured simultaneously or over a short period of time where motion is negligible. However, combining multiple frames from a moving sensor requires proper alignment of the frames to a common coordinate system. In topographic mapping using aerial ladar, it is feasible to rely on accurate Global Positioning System/inertial Navigation System (GPS/INS) based pose determination to align sensed 3D points. However, weight, cost, and real-time constraints may preclude such accurate pose measurement in certain applications. Therefore, there is a need in the art for data-driven registration of ladar frames.